Standard based mapping of industry vertical model to legacy environments

ABSTRACT

A standard based mapping of industry vertical models to legacy environments to suggest points of enterprise application integration. A representation of a first domain comprising industry model instance data is generated in a standard-based format from an enterprise industry model. A representation in the standard-based format of a second domain comprising legacy data is generated from the legacy environment. A set of inference rules is applied against the representation of enterprise industry model instance data and the representation of legacy data. One or more candidate legacy assets in the representation of legacy data capable of implementing an aspect of the enterprise industry model are identified. The identified one or candidate legacy assets for implementing the aspect of the enterprise industry model are then recommended to a user.

BACKGROUND

1. Field

The present invention relates generally to an improved data processingsystem and more specifically to a computer implemented method, system,and computer usable program code for mapping industry vertical models tolegacy environments in a standard format to suggest points of enterpriseapplication integration.

2. Description of the Related Art

The use of service-oriented architecture (SOA) environments andinformation services is fast becoming the preferred implementation forenterprise systems. Using SOA, an enterprise may be visualized as acollection of loosely coupled building blocks, called informationservices or business services. Information services provide a discretebusiness function, such as, for example, checking credit, opening anaccount, and so on, that can be adapted to a particular businesscontext. As a business expands its enterprise capabilities, moreinformation services are added to the network to accommodate theexpansion.

In almost every instance where new information services are developed tosupport a set of business needs today, there is a set of existing custombuilt applications, software packages, middleware, operating systems andhardware components that have to be understood and at least integratedwith the new services. These existing software and hardware componentsare known as “legacy” components. A legacy environment comprisescomponents that have been inherited from languages, platforms, andtechniques earlier than current technology. The process of integratingnew services into an environment comprising legacy components is knownas a “brownfield deployment”. The term “brownfield deployment” is takenfrom the building industry, where undeveloped land is described as“greenfield” and previously developed land is described as “brownfield”.A “greenfield deployment” describes a deployment in which applicationsare built in a “clean” environment with no existing components toconsider.

Large organizations often continue to operate in a legacy environmentbecause it is cost prohibitive for the organization to move to acompletely new platform. A focus of IT professionals in theseorganizations is on the mining of data about the existing (legacy)components in the system to discover all of the legacy components andthe relationships among them, as well as the automation of this datamining. This data mining is necessary as many components undergomodifications and upgrades over time, often without the associateddocumentation being updated. In addition, with the start of theretirement of the “baby boom” generation, industries are losing many ofthe only people with any detailed knowledge of these legacy components.These legacy environments are extremely complex with thousands ofdifferent components and represent significantly more complexity thanany single, or even small team of, IT professional can retain in theirhead. Consequently, there is an emerging and urgent need to find a wayto organize and visualize the information gathered through these miningefforts.

SUMMARY

The illustrative embodiments provide a standard based mapping ofindustry vertical models to legacy environments to suggest points ofenterprise application integration. A representation of a first domaincomprising industry model instance data is generated in a standard-basedformat from an enterprise industry model. A representation in thestandard-based format of a second domain comprising legacy data isgenerated from the legacy environment. A set of inference rules isapplied against the representation of enterprise industry model instancedata and the representation of legacy data. One or more candidate legacyassets in the representation of legacy data capable of implementing anaspect of the enterprise industry model are identified. The identifiedone or candidate legacy assets for implementing the aspect of theenterprise industry model are then recommended to a user.

DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a diagram of a data processing system in which illustrativeembodiments may be implemented;

FIG. 3 is an exemplary illustration of a mapping system in accordancewith an illustrative embodiment;

FIG. 4 is an exemplary illustration of a topic map based on industrymodel data in accordance with an illustrative embodiment;

FIG. 5 is an exemplary illustration of a topic map based on legacyenvironment data in accordance with an illustrative embodiment;

FIG. 6 is an exemplary topic map illustrating the mapping of an industrymodel data topic map to a legacy environment topic map in accordancewith an illustrative embodiment;

FIGS. 7A-7C illustrate an example of how connections between data storedin one repository and represented by a first topic map and data storedin another repository and represented by a second topic map may beinferred in accordance with an illustrative embodiment; and

FIG. 8 is a flowchart illustrating an exemplary process for mappingindustry vertical models to legacy environments in a standard format inaccordance with an illustrative embodiment.

DETAILED DESCRIPTION

As will be appreciated by one skilled in the art, aspects of thedisclosure may be embodied as a system, method or computer programproduct. Accordingly, aspects of the disclosure may take the form of anentirely hardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,aspects of the disclosure may take the form of a computer programproduct embodied in one or more computer readable medium(s) havingcomputer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thedisclosure may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJava, Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

Aspects of the disclosure are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or lock diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

With reference now to the figures and in particular with reference toFIGS. 1-2, exemplary diagrams of data processing environments areprovided in which illustrative embodiments may be implemented. It shouldbe appreciated that FIGS. 1-2 are only exemplary and are not intended toassert or imply any limitation with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made.

FIG. 1 depicts a pictorial representation of a network of dataprocessing systems in which illustrative embodiments may be implemented.Network data processing system 100 is a network of computers in whichthe illustrative embodiments may be implemented. Network data processingsystem 100 contains network 102, which is the medium used to providecommunications links between various devices and computers connectedtogether within network data processing system 100. Network 102 mayinclude connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server 104 and server 106 connect to network102 along with storage unit 108. In addition, clients 110, 112, and 114connect to network 102. Clients 110, 112, and 114 may be, for example,personal computers or network computers. In the depicted example, server104 provides information, such as boot files, operating system images,and applications to clients 110, 112, and 114. Clients 110, 112, and 114are clients to server 104 in this example. Network data processingsystem 100 may include additional servers, clients, and other devicesnot shown.

Program code located in network data processing system 100 may be storedon a computer recordable storage medium and downloaded to a dataprocessing system or other device for use. For example, program code maybe stored on a computer recordable storage medium on server 104 anddownloaded to client 110 over network 102 for use on client 110.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented as anumber of different types of networks, such as for example, an intranet,a local area network (LAN), or a wide area network (WAN). FIG. 1 isintended as an example, and not as an architectural limitation for thedifferent illustrative embodiments.

With reference now to FIG. 2, a diagram of a data processing system isdepicted in accordance with an illustrative embodiment. Data processingsystem 200 is an example of a computer, such as server 104 or client 110in FIG. 1, in which computer usable program code or instructionsimplementing the processes may be located for the illustrativeembodiments. In this illustrative example, data processing system 200includes communications fabric 202, which provides communicationsbetween processor unit 204, memory 206, persistent storage 208,communications unit 210, input/output (I/O) unit 212, and display 214.

Processor unit 204 serves to execute instructions for software that maybe loaded into memory 206. Processor unit 204 may be a set of one ormore processors or may be a multi-processor core, depending on theparticular implementation. Further, processor unit 204 may beimplemented using one or more heterogeneous processor systems, in whicha main processor is present with secondary processors on a single chip.As another illustrative example, processor unit 204 may be a symmetricmulti-processor system containing multiple processors of the same type.

Memory 206 and persistent storage 208 are examples of storage devices216. A storage device is any piece of hardware that is capable ofstoring information, such as, for example, without limitation, data,program code in functional form, and/or other suitable informationeither on a temporary basis and/or a permanent basis. Memory 206, inthese examples, may be, for example, a random access memory, or anyother suitable volatile or non-volatile storage device. Persistentstorage 208 may take various forms, depending on the particularimplementation. For example, persistent storage 208 may contain one ormore components or devices. For example, persistent storage 208 may be ahard drive, a flash memory, a rewritable optical disk, a rewritablemagnetic tape, or some combination of the above. The media used bypersistent storage 208 may be removable. For example, a removable harddrive may be used for persistent storage 208.

Communications unit 210, in these examples, provides for communicationwith other data processing systems or devices. In these examples,communications unit 210 is a network interface card. Communications unit210 may provide communications through the use of either or bothphysical and wireless communications links.

Input/output unit 212 allows for the input and output of data with otherdevices that may be connected to data processing system 200. Forexample, input/output unit 212 may provide a connection for user inputthrough a keyboard, a mouse, and/or some other suitable input device.Further, input/output unit 212 may send output to a printer. Display 214provides a mechanism to display information to a user.

Instructions for the operating system, applications, and/or programs maybe located in storage devices 216, which are in communication withprocessor unit 204 through communications fabric 202. In theseillustrative examples, the instructions are in a functional form onpersistent storage 208. These instructions may be loaded into memory 206for execution by processor unit 204. The processes of the differentembodiments may be performed by processor unit 204 using computerimplemented instructions, which may be located in a memory, such asmemory 206.

These instructions are referred to as program code, computer usableprogram code, or computer readable program code that may be read andexecuted by a processor in processor unit 204. The program code, in thedifferent embodiments, may be embodied on different physical or computerreadable storage media, such as memory 206 or persistent storage 208.

Program code 218 is located in a functional form on computer readablemedia 220 that is selectively removable and may be loaded onto ortransferred to data processing system 200 for execution by processorunit 204. Program code 218 and computer readable media 220 form computerprogram product 222. In one example, computer readable media 220 may becomputer readable storage media 224 or computer readable signal media226. Computer readable storage media 224 may include, for example, anoptical or magnetic disc that is inserted or placed into a drive orother device that is part of persistent storage 208 for transfer onto astorage device, such as a hard drive, that is part of persistent storage208. Computer readable storage media 224 also may take the form of apersistent storage, such as a hard drive, a thumb drive, or a flashmemory that is connected to data processing system 200. In someinstances, computer readable storage media 224 may not be removable fromdata processing system 200.

Alternatively, program code 218 may be transferred to data processingsystem 200 using computer readable signal media 226. Computer readablesignal media 226 may be, for example, a propagated data signalcontaining program code 218. For example, computer readable signal media226 may be an electro-magnetic signal, an optical signal, and/or anyother suitable type of signal. These signals may be transmitted overcommunications links, such as wireless communications links, an opticalfiber cable, a coaxial cable, a wire, and/or any other suitable type ofcommunications link. In other words, the communications link and/or theconnection may be physical or wireless in the illustrative examples. Thecomputer readable media also may take the form of non-tangible media,such as communications links or wireless transmissions containing theprogram code.

In some illustrative embodiments, program code 218 may be downloadedover a network to persistent storage 208 from another device or dataprocessing system through computer readable signal media 226 for usewithin data processing system 200. For instance, program code stored ina computer readable storage media in a server data processing system maybe downloaded over a network from the server to data processing system200. The data processing system providing program code 218 may be aserver computer, a client computer, or some other device capable ofstoring and transmitting program code 218.

The different components illustrated for data processing system 200 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments may be implemented. The different illustrativeembodiments may be implemented in a data processing system includingcomponents in addition to or in place of those illustrated for dataprocessing system 200. Other components shown in FIG. 2 can be variedfrom the illustrative examples shown. The different embodiments may beimplemented using any hardware device or system capable of executingprogram code. As one example, data processing system 200 may includeorganic components integrated with inorganic components and/or may becomprised entirely of organic components excluding a human being. Forexample, a storage device may be comprised of an organic semiconductor.

As another example, a storage device in data processing system 200 isany hardware apparatus that may store data. Memory 206, persistentstorage 208, and computer readable media 220 are examples of storagedevices in a tangible form.

In another example, a bus system may be used to implement communicationsfabric 202 and may be comprised of one or more buses, such as a systembus or an input/output bus. Of course, the bus system may be implementedusing any suitable type of architecture that provides for a transfer ofdata between different components or devices attached to the bus system.Additionally, a communications unit may include one or more devices usedto transmit and receive data, such as a modem or a network adapter.Further, a memory may be, for example, memory 206 or a cache such asfound in an interface and memory controller hub that may be present incommunications fabric 202.

As previously mentioned, legacy environments can be extremely complexwith thousands of different components. Although IT professionalscurrently use data mining processes to gather information about legacycomponents and the relationships among them, it is often difficult forusers to be able to visualize such complex systems. The illustrativeembodiments provide a solution to this problem by using a canonical datamodel approach to organize information gathered through the legacyenvironment mining efforts. In enterprise application integration, acanonical data model is a design pattern used to communicate betweendifferent data formats. The organized information gathered through thelegacy environment mining efforts is represented in a standard(canonicalized) data format, such as the topic map open standard. Atopic map conveys knowledge about resources through a superimposedlayer, or map, of the resources. The topic map ISO standard is formallyknown as ISO/IEC 13250:2003. The illustrative embodiments facilitate thecomprehension and usage of legacy data by representing the collecteddata in a standard data format and by using topic maps to enable usersto visualize a brownfield domain comprising legacy components and theirinterrelationships.

The illustrative embodiments also allow for mapping industry verticalmodels to legacy environments in a standard data format to suggestpoints of enterprise application integration. An enterprise industrymodel is an industry-specific, comprehensive enterprise modelincorporating current industry best-practices with data modeltechnology. Industry models address the unique requirements of abusiness operating in a particular industry and includeindustry-standard vocabulary to enable an organization to communicatewith others in the industry. Enterprise legacy environment datacomprises legacy data gathered through the legacy environment miningefforts. The enterprise industry model instance data and the enterpriselegacy environment data are each represented in a common standard basedformat, such as the topic map standard. The illustrative embodiments useinference rules to automatically suggest a mapping of the legacyenvironment assets that may be used to provision the instantiation ofthe industry model(s). In other words, a legacy application may beidentified as candidate asset that may be used to implement an aspect ofthe industry model. The mapping between legacy assets in the topic mapof the enterprise legacy data and elements in the topic map of theenterprise industry model instance data is used to suggest points ofenterprise application integration and enable the enterprise architectto strategically determine where to maximize investment. Thus, the reuseof legacy applications to implement an industry model may reduce costsassociated with integrating current industry standards into the legacysystem. In addition, by representing the recommended integrationinformation in a standard format, the integration information may alsobe reused by other enterprises in similar industry verticals.

The illustrative embodiments provide an advantage over existingenterprise integration methodologies in that the illustrativeembodiments provide an improved way for a user to visualize and navigatea connected web of enterprise legacy information using topic maps. Usersmay navigate through the relationship links, see patterns in theconnections, and determine where existing legacy components may bereused to provision aspects of current industry standards, and where newapplications are required to adhere to the industry standards.Representing enterprise legacy data using in the standard data format oftopic maps allows the user to see detailed local information, and alsovisualize how that information fits into a broader global context. Whileeach individual repository of industry and legacy data may effectivelyorganize and categorize its own information, the illustrativeembodiments enable relationships of topics across disconnectedrepositories to be inferred, thereby informing users of how therepository data is interconnected and allowing users to ask moremeaningful questions based upon large repositories of knowledge.

With reference now to FIG. 3, an exemplary illustration of a mappingsystem in accordance with an illustrative embodiment is shown.Enterprise application integration system 300 may be implemented in anetwork of data processing systems, such as network data processingsystem 100 in FIG. 1. Alternatively, enterprise application integrationsystem 300 may be implemented in a single data processing system, suchas data processing system 200 in FIG. 2.

Enterprise application integration system 300 is a data processingsystem that includes a plurality of software components or modules thatenterprise application integration system 300 uses to automatically mapindustry vertical models to legacy environments in a standard format tosuggest points of enterprise application integration. Enterpriseapplication integration system 300 includes industry model repository(IMR) 302, legacy asset repository 304, topic map module 306, inferenceengine 308, scoping function 322, and inference rules 324. However, itshould be noted that enterprise application integration system 300 isonly meant as an example and not intended as a limitation on differentillustrative embodiments. In other words, enterprise applicationintegration 300 may include more or fewer components as necessary toaccomplish processes of the different illustrative embodiments.

Industry model repository (IMR) 302 comprises selectable enterpriseindustry models 310 for various industry types, such as the insurance,financial, healthcare, or retail industries. Enterprise industry modelsinclude, but are not limited to, business process models, servicesmodels, domain models, data models, interface design models,requirements models, use case models, among others. An example of aspecific enterprise industry model is IBM® Insurance ApplicationArchitecture (IAA). IAA is a set of information, process, andintegration models that represent leading practice systems developmentin the insurance industry. IAA is an architectural blueprint withdetailed insurance business content that can be applied to initiativeson an enterprise-wide or specific project basis. IMR 302 may identifyrelevant industry models 310 based on context to content mapping. Forinstance, enterprise application integration system 300 provides theappropriate topic map, which is the content, to help solve the problemat hand, which is the context. The context is provided by the scope ofthe software project and by the functional and non-functionalrequirements for that software project. The scope of a software projectmay be determined by the industry, such as, for example, the insuranceindustry, financial industry, healthcare industry, retail industry, andso on, selected for the software project. In addition, the scope of thesoftware project may be determined by the architectural style, such as,for example, an SOA architectural style, a client/server architecturalstyle, distributed computing architectural style, and so on, selectedfor the software project, as well as the particular software model, suchas a use case.

Based on the context, IMR 302 provides the appropriate topic map thatwill map the context to the relevant content. For example, for aninsurance industry software project, the context may map to content inthe IAA model that enables the software engineering of an insuranceclaim processing system. IMR 302 then sends the relevant industry models310 to topic map module 306. In one embodiment, IMR 306 may beimplemented as a relational database.

Legacy asset repository 304 comprises legacy data 312 collected aboutthe existing (legacy) components in the legacy environment for aparticular industry type. The collected legacy data comprisesinformation about legacy components in the system and the relationshipsamong the components discovered in a manual or automated mining process.These legacy components may include existing software applications,middleware, operating systems, and hardware components. The legacyenvironment is known as a brownfield environment, as the integration ofany new service into the environment must take into account the existinglegacy components. Legacy asset repository 304 sends legacy data 312 totopic map module 306.

Topic map module 306 comprises a software component for generating topicmaps based on the ISO standard. A topic map comprises information usingtopics that may represent any concept from people, countries, andorganizations to software modules, individual files, and events.Associations between the topics within the topic map represent howtopics relate to one another. In addition, the topic map maintainsoccurrences of the topics that represent where the topics may be found,such as a Webpage, a uniform resource locator (URL), or a referencewithin a book. Thus, topic maps are similar to semantic networks,concept maps, and mind maps. For instance, a legacy topic generated bytopic map module 306 may comprise three main pieces of information—thename of the legacy components, the connections of the legacy componentsto each other, and also the occurrences of the legacy components (whichmaps to their physical existence on the deployment network).

Topic map module 306 generates a topic map for industry model instancedata 310 received from IMR 302 and a topic map for legacy data 312received from legacy asset repository 304. In one embodiment, the topicmaps may be built utilizing a semantic web tool called Protégé. Protégéis a free, open source ontology editor and knowledge-base framework.Protégé allows the topic maps to be built and/or modified using avariety of formats, including Resource Description Frameworks (RDFs) andWeb Ontology Language (OWL). RDF is a language for representinginformation about resources in the World Wide Web. An RDF ontology mayinclude descriptions about web resources in the form ofsubject-predicate-object expressions, called ‘triples’ in RDFterminology. The subject denotes the resource, and the predicate denotestraits or aspects of the resource and expresses a relationship betweenthe subject and the object. OWL is a language that may represententity-relationship models and constraints. An OWL ontology may includedescriptions of classes, along with their related properties andinstances. OWL may be designed for use by applications that need toprocess the content of information and facilitates greater machineinterpretability of web content by providing additional vocabulary alongwith formal semantics. Both RDF and OWL describe information in a formalway that a machine can understand, while topic maps describe theinformation in a way that humans can understand. Topic map module 306sends industry model instance topic map 314 and legacy environment topicmap 316 to inference engine 308.

Topic map module 306 may also use scoping function 322 that enables auser to define topics comprising a certain scope. A scope is a subset oftopics in a repository that are applicable to a particular task or in aparticular context. A data repository, such as a model repository, maycomprise a large number of scopes, including, for example, models forinsurance, finance, retail, deployment, enterprise, etc. All of themodels may reside the same repository, but each model has a differentscope. For instance, all models comprising a financial context would bein one scope, all the models comprising a retail context would be inanother scope, and so on. Consequently, even though there is a largesubset of information in the repository, the topic map allows forviewing the information in terms of the subset of relevant informationfor a particular task or in a particular context. Scoping allows forviewing a subset of information relevant at that time in that context.Scoping may be used to reduce the number of topics to a subset ofrelevant topics for processing by inferencing engine 308.

Inference engine 308 is a rules engine used to suggest or infer pointsof integration between the legacy environment and the industry modelinstance data. These points of integration define relationships orconnections among topics represented in the topic maps. In oneembodiment, inference engine 308 may, for example, be a commerciallyavailable product, such as Agent Building and Learning Environment(ABLE), which is available from the IBM Corporation. ABLE is a Java™framework, component library, and productivity tool kit for buildingintelligent agents using machine learning and reasoning. The ABLEframework provides a set of Java™ interfaces and base classes used tobuild a library of JavaBeans™ called AbleBeans. Java and all Java-basedtrademarks and logos are trademarks of Sun Microsystems, Inc. in theUnited States, other countries, or both. The library includes AbleBeansfor reading and writing text and database data, for data transformationand scaling, for rule-based inferencing using Boolean and fuzzy logic,and for machine learning techniques, such as neural networks, Bayesianclassifiers, and decision trees. Rule sets created using the ABLE RuleLanguage may be used by any of the provided inference engines, whichrange from simple if-then scripting to light-weight inferencing toheavy-weight artificial intelligence (AI) algorithms using patternmatching and unification.

Inference engine 308 provides the ability to automatically apply a setof inference rules derived from information about a particular industryvertical to the selected industry model instance topic map 314 andlegacy environment topic map 316 received from topic map module 306 tomap relationships between the legacy environment and the industry modelinstance data. These mapped relationships comprise recommendations ofcandidate legacy assets 318 that may be used to provision theinstantiation of the relevant industry models by leveraging machinelearning and reasoning. Legacy assets may include software applications,middleware, operating systems, and hardware components in the legacyenvironment.

Connections between topics across repositories may be created in one ofthree ways. First, an inferred connection or relationship between topicsmay be created explicitly, such as, by a user who assigns a connectionbetween a topic in, for example, repository A and another element inrepository B. Second, an explicit connection may also be created by anapplication (in this case, an overseeing computer program) which useshistorical information of previously assigned relationships to assign aconnection between topics. This historical information may be obtainedfrom a database of previous instances of inter-repository topicconnections. Third, an inferred connection or relationship betweentopics may also be created implicitly by an application using inferenceengine 308 to infer new connections or relationships between topics.Inference engine 308 uses a set of inference rules 324 based on thedomain knowledge for a particular industry vertical.

Each topic is assigned a tag that describes an attribute of the topic.The topic (subject), associated tag (object), and the relationshipbetween the topic and tag (predicate) form a subject-predicate-objectexpression, or triplet in RDF terminology. The inference engine examinesthe triplets in the topic maps to determine if the engine can createinferences between triplets to create new connections across topic maps(and repositories).

An inference probability score may also be assigned to a createdinference to indicate the probability that topic A in repository A isactually connected to topic B in repository B. In the first case above,a user instructs that a connection between topic A in repository A withtopic B in repository B be created. In this example, inference engine308 may assign an inference probability score of 100% between topic Aand topic B, as the topic in repository A is definitely connected to thetopic in repository B based on the user input. In the second case above,inference engine 308 may use prior historical information to createinferences between topics. For example, topic A (a service) inrepository A has been assigned a connection to topic B (a legacy asset)in repository B in 15 out of 20 service engagements. In other words, theparticular legacy asset has been used to implement the particularservice 15 out of 20 times. In this example, inference engine 308 mayassign an inference probability score of 75%, as there is a 75%probability that if topic A is in repository A and topic B is inrepository B, topic A and topic B are connected based on historicalservice engagement data. In the third case above, inference engine 308does not utilize historical data to infer connections or relationships,but rather infers new connections between topics based on a probability.For example, if topic A in repository A is surrounded by topics similarto the topics surrounding topic B in repository B, inference engine 308may infer that there is a probable connection between topic A inrepository A and topic B in repository B. The probability that topic Ais related to topic B may be determined by inference engine 308 by anumber of factors, such as the number of similar topics that surroundboth topic A and topic B.

It should be noted that inference engine 308 may also use Bayesianprobabilities, which are adaptive probabilities that specify some priorprobabilities that may be updated in light of new relevant data. In thisembodiment, inference engine 308 may continuously calculate theprobabilities based upon previous experience depending upon if anothertag is added to or removed from a topic.

Once inference engine 308 applies the inference rules to industry modelinstance topic map 314 and legacy environment topic map 316 and providesrecommendations of candidate legacy assets 318 that may be used toimplement aspects of the relevant industry models, topic map module 306retrieves the recommended legacy asset candidate information andgenerates a topic map using the industry model instance topic map 314,and legacy environment topic map 316, and the recommended candidatelegacy assets 318. Topic map 320 provides a graphical representationthat enables the enterprise architect to visualize theinterrelationships between the industry model instance topic map 314 andlegacy environment topic map 316, as well as the points of integrationrepresented by the candidate legacy assets 318 recommended to implementparticular aspects of the industry models.

FIG. 4 is an exemplary illustration of a topic map based on industrymodel data in accordance with an illustrative embodiment. A topic mapcomprises a representation of knowledge consisting of a graph of topics,associations, and occurrences. Topics may represent any concept,including people, countries, and organizations to software modules,individual files, and events. Associations represent the relationshipsbetween the topics. Occurrences represent information resources that arerelevant to the topics in some way.

Industry model topic map 400 is a visual representation of enterpriseindustry model instance data in a common standard based format. Industrymodel topic map 400 may be used to represent all industry model data inIMR 302 in FIG. 3 or a selected portion of the industry model data.Visualization of the industry model data knowledge base often beginswith the selection of a topic or topics that a user wants to learnabout. In the simplest cases, this selection may be accomplished by theuser naming a topic. This selection may be performed by the userentering a word or phrase into a topic-search engine. The visualizationinterface then displays a map of the area of topic space the userselects. In this illustrative example, the user has entered the phrase“Uc05 submit order” into the topic-search engine to generate industrymodel topic map 400. Industry model topic map 400 comprises severaltopics based on the search criteria and as derived from enterpriseindustry model instance data 310 in FIG. 3. Topics include use case 5(Uc05) submit order 402, RAM occurrence 404, use case 1 (Uc01) order tobill 406, customer relationship management 408, and order handling 410.Use case 5 submit order 402 is shown to have a relationship with each ofRAM occurrence 404, use case 1 order to bill 406, customer relationshipmanagement 408, and order handling 410. Customer relationship management408 is also shown to have a relationship use case 1 order to bill 406and order handling 410.

FIG. 5 is an exemplary illustration of a topic map based on legacyenvironment data in accordance with an illustrative embodiment. Legacyasset topic map 500 is a visual representation of enterprise legacy datain a common standard based format. All of the legacy assets in thelegacy environment may be modeled, using brownfield terms, as concepts.Entering the term “concept” into the topic-search engine generatesbrownfield or legacy asset topic map 500. Legacy asset topic map 500 maybe used to represent all of the legacy assets in the legacy environmentor a selected portion of the assets in the legacy environment.

In this illustrative example, legacy asset topic map 500 is shown tocomprise several topics of different types based on the search criteriaand as derived from enterprise legacy data 312 in FIG. 3. Topics includevarious concept types, including concept 502, component A 504, componentB 506, component C 508, Interface 1 510, and Node 1 512. Each concept inthe topic map is shown to be associated with one or more other conceptsin legacy asset topic map 500. For example, component C 508 may be alegacy asset that comprises a billing system used by atelecommunications company. Component C 508 is shown to be associatedwith legacy asset Interface 1 510. For instance, component C 508 mayutilize Interface 1 510 to expose an interface of service contracts tobe referenced by the billing system. Component C 508 and Interface 1 510are also associated with Node 1 512, as Interface 1 510 may run on amachine or server Node 1 512.

FIG. 6 is an exemplary topic map illustrating the mapping of an industrymodel data topic map to a legacy environment topic map in accordancewith an illustrative embodiment. Topic map 600 illustrates theinterrelationships between industry model topic map 400 in FIG. 4 andlegacy asset topic map 500 in FIG. 5. Topic map 600 may be generatedusing a set of inference rules based on domain knowledge based on theparticular industry associated with the legacy environment. Theinference rules derived from the industry model data are applied by RDFinference engine 308 in FIG. 3 to the legacy data described in the OWLontology to suggest a mapping of the legacy environment applicationsthat may be used to provision the instantiation of the industrymodel(s). This mapping may be displayed graphically in topic map 600. Avisual inspection and comparison of the suggested mapping in topic map600 may then be performed by the enterprise architect to determine whereto maximize the investment around the points of integration through thereuse of legacy assets to implement aspects of the industry models.

Topic map 600 comprises two domains—the first domain comprises theindustry model domain 602, and the second domain comprises thebrownfield domain 604. Topic map 600 illustrates a probability ofmapping from one domain to another. In this illustrative example,inference engine 308 in FIG. 3 has determined that use case 1 (Uc01)order to bill 606 is shown to have a 20% probability of beingimplemented by component B 608, and an 80% probability of beingimplemented by component C 610. Similarly, use case 5 (Uc05) submitorder 612 is shown to have a 95% probability of being implemented bycomponent A 614. Thus, the enterprise architect may utilize topic map600 to identify that legacy asset component C 610 may be a candidate forimplementing industry standard process use case 1 (Uc01) order to bill606. Likewise, topic map 600 allows the enterprise architect to identifythat component A 614 may be a candidate for implementing industrystandard process use case 5 (Uc05) submit order 612.

FIGS. 7A-7C illustrate a simple example of how connections between datastored in one repository and represented by a first topic map and datastored in another repository and represented by a second topic map maybe inferred in accordance with an illustrative embodiment. Theseconnections may be inferred using an inference engine, such as inferenceengine 308 in FIG. 3. Although the illustrative example in FIGS. 7A-7Cis described in terms of inferring a connection between two people, thisillustrative example may be applied equally well to creating inferredconnections between topics in one or more repositories. Theserepositories may include, for example, industry model repository 302 andlegacy asset repository 304 in FIG. 3 comprising industry model andlegacy asset related topics.

FIG. 7A illustrates an exemplary topic map 700 created by topic mapmodule 306 in FIG. 3 from a first repository (repository A). RepositoryA is a storage location in which users may store and post photos. A user(Person 1 702) posts Shorty the dog picture 704 to repository A, whichis specified as a picture 706 of the user's dog. The user also attachesthe following tags to picture 704—Shorty 708 (name of the dog), basset710, and dog 712. In this example, all of the associations 708-712 areweighted at 100% probability, as there is no uncertainty in theserelationships.

FIG. 7B illustrates another exemplary topic map 720 created by topic mapmodule 306 in FIG. 3 from a second repository (repository B). RepositoryB is a storage location in which users may store and tag bookmarks. Auser (Person 2 722) posts a bookmark “bigears.com” 724 to repository B,which is specified as a bookmark 726. The user also attaches thefollowing tags to bookmark bigears.com 724—basset 728 and dog 730. Inthis example, all of the associations 728 and 730 are again weighted at100% probability, as there is no uncertainty in these relationships.

FIG. 7C illustrates an exemplary topic map illustrating howrelationships between topic maps may be inferred in accordance with anillustrative embodiment. Topic map 740 illustrates the interrelationshipbetween topic map 700 in FIG. 7A and topic map 720 in FIG. 7B. Topic map740 may be generated using a set of inference rules applied by inferenceengine 308 in FIG. 3 to the repository data to infer connections betweentopics in one domain (e.g., photo model domain repository A) and topicsin another domain (bookmark model domain in repository B), as well asthe probability score of these inferred connections.

To infer relationships between topics in repository A and repository Bcomprising different domains, a rule is created and used by theinference engine to examine the tags surrounding a topic. The tagexamination may comprise determining how many tags are associated with atopic, and how many of the tags associated with a topic match (are thesame as) other tags associated with topics in another repository. Basedon this tag examination, the inference engine determines whether a topiccan be mapped in one repository to a topic in another repository. Inthis simple example, the inference engine may determine whether thetopic Person 1 742 in repository A and the topic Person 2 744 inrepository B are related (i.e., the same person). The inference enginedetects that Person 1 742 has placed an item into repository A with 3tags—Shorty 746, basset 748, dog 750, and that Person 2 744 has placedan item into repository B with 2 tags—basset 752, dog 754. The inferenceengine uses the rule to determine that two of the tags (basset 752, dog754) from the item provided by Person 2 744 in repository B matches twoof the three tags (basset 748, dog 750) from the item provided by Person1 in repository A. Thus, as Person 1 742 is posting pictures inrepository A and using a similar set of tags as Person 2 744 postingbookmarks in repository B, the inference engine may calculate theprobability of how connected the Person 1 and Person 2 topics are basedupon the number of tag matches for the topic. Since two out of the threetags of Person 1 742 are matched to the tags of Person 2 744, theinference engine may infer that Person 1 742 is related to Person 2 744with a probability of 66.6% and create an association 756 specifyingthis probability between Person 1 742 and Person 2 744. A user viewingthe intersected topic map 740 may or may not conclude that Person 1 742and Person 2 744 are the same person based on how high the probabilityof association 756 is weighted in topic map 740.

FIG. 8 is a flowchart illustrating an exemplary process for mappingindustry vertical models to legacy environments in a standard format inaccordance with an illustrative embodiment. The process shown in FIG. 8may be implemented in enterprise application integration system 300 inFIG. 3.

The process begins when the enterprise application integration systemreceives an input from a user, such as an enterprise architect,requesting legacy asset candidates for use in provisioning industry datamodels (step 802). After receiving the input to generate such a mappingin step 802, the enterprise application integration system receives aselection by the enterprise architect of relevant enterprise industrymodel data, such as industry model data 310 in FIG. 3, for use in therequested mapping recommendations (step 804). In addition, theenterprise application integration system also receives legacy data,such as legacy data 312 in FIG. 3, collected about assets in the legacy(brownfield) environment (step 806).

A topic map module, such as topic map module 306 in FIG. 3, retrievesthe relevant enterprise industry model data and creates an industrymodel topic map representing the enterprise industry model instance datain a standard format (step 808). The topic map module also retrieves thelegacy data for the enterprise legacy environment and creates a legacyasset topic map representing the legacy data in a same standard formatas the industry model topic map (step 810).

Once topic maps for both the enterprise industry model data and thelegacy data have been created, an inference rules engine retrieves theindustry model topic map and the legacy asset topic map and applies aset of industry-specific inference rules to the industry model instancedata in the industry model topic map and the legacy asset information inthe legacy asset topic map (step 812). The set of industry-specificinference rules may be based on the domain knowledge for the particularindustry vertical associated with the legacy environment. Using theapplied inference rules, the inference rules engine identifies andsuggests points of integration between the industry model topic map andthe legacy asset topic map (step 814). The points of integration theindustry model topic map and the legacy asset topic map identifycandidate legacy assets in the brownfield environment that may be usedto implement one or aspects of the industry model(s). The topic mapmodule retrieves the candidate legacy asset recommendations from theinference rules engine and creates a recommendation topic map thatprovides a graphical view of the candidate legacy assets that may beused to implement one or aspects of the industry model (step 816). Therecommendation topic map is displayed to the enterprise architect, whomay then utilize the recommendations in the recommendation topic map toselect a candidate legacy asset to use to implement one or more aspectsof the industry models (step 818). It should be noted that the decisionby the enterprise architect to select a particular legacy asset toimplement aspects of the industry model may also be used to update theset of rules utilized by inference engine 308 in FIG. 3 for theparticular industry model.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the embodimentsof the disclosure. As used herein, the singular forms “a”, “an” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. It will be further understood thatthe terms “comprises” and/or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the embodiments of the disclosure has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the embodiments of the disclosure in the formsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the disclosure. The embodiments were chosen and described in order tobest explain the principles of the disclosure and the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

The embodiments of the disclosure can take the form of an entirelyhardware embodiment, an entirely software embodiment or an embodimentcontaining both hardware and software elements. In a preferredembodiment, the disclosure is implemented in software, which includesbut is not limited to firmware, resident software, microcode, etc.

Furthermore, the embodiments of the disclosure can take the form of acomputer program product accessible from a computer readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. For the purposes of this description,a computer readable medium can be any tangible apparatus that cancontain, store, communicate, propagate, or transport the program for useby or in connection with the instruction execution system, apparatus, ordevice.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk—read only memory (CD-ROM), compactdisk—read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters. The description of theembodiments of the disclosure has been presented for purposes ofillustration and description, and is not intended to be exhaustive orlimited to the embodiments of the disclosure in the forms disclosed.Many modifications and variations will be apparent to those of ordinaryskill in the art. The embodiments were chosen and described in order tobest explain the principles of the disclosure, the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

What is claimed is:
 1. A method for recommending points of integrationbetween an enterprise industry model and a legacy model, the methodcomprising: receiving a user entered first search term for theenterprise industry model; electronically searching a first domain ofinstance data of the enterprise industry model for first nodes matchingthe first search term; generating and displaying a first graphical mapthat shows one or more first nodes that match the first search term andedges which interconnect the one or more first nodes with other nodes inaccordance with relationships defined in the first domain; receiving auser entered second search term for the legacy model; electronicallysearching a second domain of instance data of the legacy model forsecond nodes matching the second search term; generating and displayinga second graphical map that shows one or more of the second nodes thatmatch the second search term and edges which interconnect the one ormore second nodes with other nodes in accordance with relationshipsdefined in the second domain; calculating a probability scorerepresenting a probability that a first one of the matching second nodesis capable of implementing the function of a first one of the matchingfirst nodes; and graphically indicating a connection between and theprobability score for the first one of the matching first nodes and thefirst one of the matching second nodes.
 2. The method of claim 1,wherein said first graphical map includes one or more additional nodesnot matching the first search term that are connected to at least one ofthe first nodes by an edge, wherein said second graphical map includesone or more additional nodes not matching the second search term thatare connected to at least one of the second nodes by an edge.
 3. Themethod of claim 1, wherein calculating the probability score comprisesone or more of: (a) determining that a user explicitly indicated aconnection between the first one of the second nodes and the first oneof the first nodes and responsively setting the probability score to onehundred percent; (b) determining from historical information anexistence of a plurality of recorded situations in which a map for thefirst domain includes the first one of the first nodes and in which amap for the second domain includes the first one of the second nodes;computing a first quantity of times representing a sub-quantity of therecorded situations where the first one of the first nodes was connectedto the first one of the second nodes; computing a second quantity oftimes representing a sub-quantity of the recorded situations where thefirst one of the first nodes was not connected to the second one of thesecond nodes; and calculating the probability score from the computedfirst quantity and the computed second quantity; and (c) determining theprobability score based on a level of similarity that a first set ofnodes having an edge to the first one of the first nodes have to asecond set of nodes having an edge to the first one of the second nodes.4. The method of claim 1, wherein the first graphical map is a topic maprepresentation showing a set of first nodes and their interrelationshipsin accordance with topic map recorded information stored for the firstdomain, wherein the second graphical map is topic map representationshowing a set of second nodes and their interrelationships in accordancewith topic map recorded information stored for the second domain.
 5. Themethod of claim 1, further comprising: creating the first graphical mapfrom a first topic map; creating the second graphical map from a secondtopic map; creating a combined topic map view comprising arepresentation of a subset of the instance data of the enterpriseindustry model and a representation of a subset of the instance data ofthe legacy model, wherein the combined topic map view indicates arelationship between the first one of the first nodes and the first oneof the second nodes.
 6. The method of claim 5, further comprising:applying a set of inference rules against the representation ofenterprise industry model and the representation of legacy model by:examining tags associated with the first one of the first nodes and tagsassociated with the first one of the second nodes; determining whetherthe tags associated with the first one of the first nodes match any ofthe tags associated with the first one of the second nodes; responsiveto determining a match, creating the relationship between the first oneof the first nodes and the first one of the second nodes; and assigningthe probability score to the relationship.
 7. The method of claim 1,wherein the first graphical map, the second graphical map, and theprobability score are concurrently shown within a single graphical userinterface.
 8. An apparatus comprising: a bus; a storage device connectedto the bus, wherein the storage device contains computer readable code;a communications unit connected to the bus; and a processing unitconnected to the bus, wherein the processing unit executes the computerreadable code to: receive a user entered first search term for theenterprise industry model; electronically search a first domain ofinstance data of the enterprise industry model for first nodes matchingthe first search term; generate and displaying a first graphical mapthat shows one or more first nodes that match the first search term andedges which interconnect the one or more first nodes with other nodes inaccordance with relationships defined in the first domain; receive auser entered second search term for the legacy model; electronicallysearch a second domain of instance data of the legacy model for secondnodes matching the second search term; generate and displaying a secondgraphical map that shows one or more of the second nodes that match thesecond search term and edges which interconnect the one or more secondnodes with other nodes in accordance with relationships defined in thesecond domain; calculate a probability score representing a probabilitythat a first one of the matching second nodes is capable of implementingthe function of a first one of the matching first nodes; and graphicallyindicate a connection between and the probability score for the firstone of the matching first nodes and the first one of the matching secondnodes.
 9. The apparatus of claim 8, wherein said first graphical mapincludes one or more additional nodes not matching the first search termthat are connected to at least one of the first nodes by an edge,wherein said second graphical map includes one or more additional nodesnot matching the second search term that are connected to at least oneof the second nodes by an edge.
 10. The apparatus of claim 8, whereinthe processing unit executes the computer readable code to calculate theprobability score by one or more of: calculating the probability scorecomprises one or more of: (a) determining that a user explicitlyindicated a connection between the first one of the second nodes and thefirst one of the first nodes and responsively setting the probabilityscore to one hundred percent; (b) determining from historicalinformation an existence of a plurality of recorded situations in whicha map for the first domain includes the first one of the first nodes andin which a map for the second domain includes the first one of thesecond nodes; computing a first quantity of times representing asub-quantity of the recorded situations where the first one of the firstnodes was connected to the first one of the second nodes; computing asecond quantity of times representing a sub-quantity of the recordedsituations where the first one of the first nodes was not connected tothe second one of the second nodes; and calculating the probabilityscore from the computed first quantity and the computed second quantity;and (c) determining the probability score based on a level of similaritythat a first set of nodes having an edge to the first one of the firstnodes have to a second set of nodes having an edge to the first one ofthe second nodes.
 11. The apparatus of claim 8, wherein the processingunit further executes the computer readable code to: create the firstgraphical map from a first topic map; create the second graphical mapfrom a second topic map; creating a combined topic map view comprising arepresentation of a subset of the instance data of the enterpriseindustry model and a representation of a subset of the instance data ofthe legacy model, wherein the combined topic map view indicates arelationship between the first one of the first nodes and the first oneof the second nodes.
 12. The apparatus of claim 11, wherein theprocessing unit further executes the computer readable code to apply aset of inference rules against the representation of enterprise industrymodel and the representation of legacy model by: examining tagsassociated with the first one of the first nodes and tags associatedwith the first one of the second nodes; determining whether the tagsassociated with the first one of the first nodes match any of the tagsassociated with the first one of the second nodes; responsive todetermining a match, creating the relationship between the first one ofthe first nodes and the first one of the second nodes; and assigning theprobability score to the relationship.
 13. The apparatus of claim 8,wherein the first graphical map, the second graphical map, and theprobability score are concurrently shown within a single graphical userinterface.
 14. A computer program product for recommending points ofintegration between an enterprise industry model and a legacyenvironment, the computer program product comprising: a non-transitorycomputer readable storage device storing computer readable program codethe computer readable program code comprising: computer readable programcode for receiving a user entered first search term for the enterpriseindustry model; computer readable program code for searching a firstdomain of instance data of the enterprise industry model for first nodesmatching the first search term; computer readable program code forgenerating and displaying a first graphical map that shows one or morefirst nodes that match the first search term and edges whichinterconnect the one or more first nodes with other nodes in accordancewith relationships defined in the first domain; computer readableprogram code for receiving a user entered second search term for thelegacy model; computer readable program code for searching a seconddomain of instance data of the legacy model for second nodes matchingthe second search term; computer readable program code for generatingand displaying a second graphical map that shows one or more of thesecond nodes that match the second search term and edges whichinterconnect the one or more second nodes with other nodes in accordancewith relationships defined in the second domain; computer readableprogram code for calculating a probability score representing aprobability that a first one of the matching second nodes is capable ofimplementing the function of a first one of the matching first nodes;and computer readable program code for graphically indicating aconnection between and the probability score for the first one of thematching first nodes and the first one of the matching second nodes. 15.The computer program product of claim 14, wherein said first graphicalmap includes one or more additional nodes not matching the first searchterm that are connected to at least one of the first nodes by an edge,wherein said second graphical map includes one or more additional nodesnot matching the second search term that are connected to at least oneof the second nodes by an edge.
 16. The computer program product ofclaim 14, wherein the computer readable program code for calculating theprobability score is for one or more of: (a) determining that a userexplicitly indicated a connection between the first one of the secondnodes and the first one of the first nodes and responsively setting theprobability score to one hundred percent; (b) determining fromhistorical information an existence of a plurality of recordedsituations in which a map for the first domain includes the first one ofthe first nodes and in which a map for the second domain includes thefirst one of the second nodes; computing a first quantity of timesrepresenting a sub-quantity of the recorded situations where the firstone of the first nodes was connected to the first one of the secondnodes; computing a second quantity of times representing a sub-quantityof the recorded situations where the first one of the first nodes wasnot connected to the second one of the second nodes; and calculating theprobability score from the computed first quantity and the computedsecond quantity; and (c) determining the probability score based on alevel of similarity that a first set of nodes having an edge to thefirst one of the first nodes have to a second set of nodes having anedge to the first one of the second nodes.
 17. The computer programproduct of claim 16, wherein the first graphical map is a topic maprepresentation showing a set of first nodes and their interrelationshipsin accordance with topic map recorded information stored for the firstdomain, wherein the second graphical map is topic map representationshowing a set of second nodes and their interrelationships in accordancewith topic map recorded information stored for the second domain. 18.The computer program product of claim 16, wherein the computer readableprogram code further comprises: computer readable program code forcreating the first graphical map from a first topic map; computerreadable program code for creating the second graphical map from asecond topic map; computer readable program code for creating a combinedtopic map view comprising a representation of a subset of the instancedata of the enterprise industry model and a representation of a subsetof the instance data of the legacy model, wherein the combined topic mapview indicates a relationship between the first one of the first nodesand the first one of the second nodes.
 19. The computer program productof claim 18, wherein the computer readable program code furthercomprising: computer readable program code for applying a set ofinference rules against the representation of enterprise industry modeland the representation of legacy model by: examining tags associatedwith the first one of the first nodes and tags associated with the firstone of the second nodes; determining whether the tags associated withthe first one of the first nodes match any of the tags associated withthe first one of the second nodes; responsive to determining a match,creating the relationship between the first one of the first nodes andthe first one of the second nodes; and assigning the probability scoreto the relationship.
 20. The computer program product of claim 14,wherein the first graphical map, the second graphical map, and theprobability score are concurrently shown within a single graphical userinterface.